FABIO JULIANO DA SILVA LOPES

FABIO JULIANO DA SILVA LOPES

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Collaborative development of the Lidar Processing Pipeline (LPP) for retrievals of atmospheric aerosols and clouds
2023 - PALLOTTA, JUAN V.; CARVALHO, SILVANIA A. de; LOPES, FABIO J. da S.; CACHEFFO, ALEXANDRE; LANDULFO, EDUARDO; BARBOSA, HENRIQUE M.J.
Atmospheric lidars can simultaneously measure clouds and aerosols with high temporal and spatial resolution and hence help understand cloud–aerosol interactions, which are the source of major uncertainties in future climate projections. However, atmospheric lidars are typically custom-built, with significant differences between them. In this sense, lidar networks play a crucial role as they coordinate the efforts of different groups, provide guidelines for quality-assured routine measurements and opportunities for side-by-side instrument comparisons, and enforce algorithm validation, all aiming to homogenize the physical retrievals from heterogeneous instruments in a network. Here we provide a high-level overview of the Lidar Processing Pipeline (LPP), an ongoing, collaborative, and open-source coordinated effort in Latin America. The LPP is a collection of tools with the ultimate goal of handling all the steps of a typical analysis of lidar measurements. The modular and configurable framework is generic enough to be applicable to any lidar instrument. The first publicly released version of the LPP produces data files at levels 0 (raw and metadata), 1 (averaging and layer mask), and 2 (aerosol optical properties). We assess the performance of the LPP through quantitative and qualitative analyses of simulated and measured elastic lidar signals. For noiseless synthetic 532 nm elastic signals with a constant lidar ratio (LR), the root mean square error (RMSE) in aerosol extinction within the boundary layer is about 0.1 %. In contrast, retrievals of aerosol backscatter from noisy elastic signals with a variable LR have an RMSE of 11 %, mostly due to assuming a constant LR in the inversion. The application of the LPP for measurements in São Paulo, further constrained by co-located AERONET data, retrieved a lidar ratio of 69.9 ± 5.2 sr at 532 nm, in agreement with reported values for urban aerosols. Over the Amazon, analysis of a 6 km thick multi-layer cirrus found a cloud optical depth of about 0.46, also in agreement with previous studies. From this exercise, we identify the need for new features and discuss a roadmap to guide future development, accommodating the needs of our community.
Analyzing the influence of vehicular traffic on the concentration of pollutants in the city of São Paulo
2023 - MOREIRA, GREGORI de A.; CACHEFFO, ALEXANDRE; ANDRADE, IZABEL da S.; LOPES, FABIO JULIANO da S.; GOMES, ANTONIO A.; LANDULFO, EDUARDO
This study employs surface and remote sensing data jointly with deep learning techniques to examine the influence of vehicular traffic in the seasonal patterns of CO, NO2 , PM2.5, and PM10 concentrations in the São Paulo municipality, as the period of physical distancing (March 2020 to December 2021), due to SARS-CoV-2 pandemic and the resumption of activities, made it possible to observe significant variations in the flow of vehicles in the city of São Paulo. Firstly, an analysis of the planetary boundary layer height and ventilation coefficient was performed to identify the seasons’ patterns of pollution dispersion. Then, the variations (from 2018 to 2021) of the seasonal average values of air temperature, relative humidity, precipitation, and thermal inversion occurrence/position were compared to identify possible variations in the patterns of such variables that would justify (or deny) the occurrence of more favorable conditions for pollutants dispersion. However, no significant variations were found. Finally, the seasonal average concentrations of the previously mentioned pollutants were compared from 2018 to 2021, and the daily concentrations observed during the pandemic period were compared with a model based on an artificial neural network. Regarding the concentration of pollutants, the primarily sourced from vehicular traffic (CO and NO2 ) exhibited substantial variations, demonstrating an inverse relationship with the rate of social distancing. In addition, the measured concentrations deviated from the predictive model during periods of significant social isolation. Conversely, pollutants that were not primarily linked to vehicular sources (PM2.5 and PM10) exhibited minimal variation from 2018 to 2021; thus, their measured concentration remained consistent with the prediction model.
Assessing spatial variation of PBL height and aerosol layer aloft in São Paulo Megacity using simultaneously two lidar during winter 2019
2022 - MOREIRA, GREGORI de A.; OLIVEIRA, AMAURI P. de; CODATO, GEORGIA; SANCHEZ, MACIEL P.; TITO, JANET V.; SILVA, LEONARDO A.H. e; SILVEIRA, LUCAS C. da; SILVA, JONATAN J. da; LOPES, FABIO J. da S.; LANDULFO, EDUARDO
This work presents the use of two elastic lidar systems to assess the horizontal variation of the PBL height (PBLH) and aerosol layer aloft in the São Paulo Megacity. These two lidars performed simultaneous measurements 10.7 km apart in a highly urbanized and relatively flat area of São Paulo for two winter months of 2019. The results showed that the PBLH differences display diurnal variation that depends on the PBL during daytime growth phases. Cloud and sea breeze effects control most of PBLH variation. In the absence of cloud and sea breeze, the maximum difference (~300 m) occurs in the rapid development stage and is due to topographic effects. When the PBL approaches its maximum daily value, it tends to level off with respect to the topography. In addition, it was presented a method that combines elastic lidar (to detect an aerosol layer) and satellite data (to classify such a layer from Aerosol Optical Depth (AOD) and Aerosol Index (AI) information) for the detection of biomass burning events. This methodology demonstrated that the variations caused by Biomass Burning in AOD and AI enable both the detection of aerosol plumes originating from biomass burning and the identification of their origin.
Analysis of atmospheric aerosol optical properties in the northeast Brazilian atmosphere with remote sensing data from MODIS and CALIOP/CALIPSO satellites, AERONET photometers and a ground-based Lidar
2019 - OLIVEIRA, ALINE M. de; SOUZA, CRISTINA T.; OLIVEIRA, NARA P.M. de; MELO, ALINE K.S.; LOPES, FABIO J.S.; LANDULFO, EDUARDO; ELBERN, HENDRIK; HOELZEMANN, JUDITH J.
A 12-year analysis, from 2005 to 2016, of atmospheric aerosol optical properties focusing for the first time on Northeast Brazil (NEB) was performed based on four di erent remote sensing datasets: the Moderate Resolution Imaging Spectroradiometer (MODIS), the Aerosol Robotic Network (AERONET), the Cloud-Aerosol LIDAR with Orthogonal Polarization (CALIOP) and a ground-based Lidar from Natal. We evaluated and identified distinct aerosol types, considering Aerosol Optical Depth (AOD) and Angström Exponent (AE). All analyses show that over the NEB, a low aerosol scenario prevails, while there are two distinct seasons of more elevated AOD that occur every year, from August to October and January to March. According to MODIS, AOD values ranges from 0.04 to 0.52 over the region with a mean of 0.20 and occasionally isolated outliers of up to 1.21. Aerosol types were identified as sea spray, biomass burning, and dust aerosols mostly transported from tropical Africa. Three case studies on days with elevated AOD were performed. All cases identified the same aerosol types and modeled HYSPLIT backward trajectories confirmed their source-dependent origins. This analysis is motivated by the implementation of an atmospheric chemistry model with an advanced data assimilation system that will use the observational database over NEB with the model to overcome high uncertainties in the model results induced by still unvalidated emission inventories.
Latin American Lidar Network (LALINET) for aerosol research: diagnosis on network instrumentation
2016 - GUERRERO-RASCADO, J.L.; LANDULFO, EDUARDO; ANTUNA, JUAN C.; BARBOSA, HENRIQUE de M.J.; BARJA, BORIS; BATIDAS, ALVARO E.; BEDOYA, ANDRES E.; COSTA, RENATA F. da; ESTEVAN, RENE; FORNO, RICARDO; GOUVEIA, DIEGO A.; JIMENEZ, CRISTOFER; LARROZA, ELIANE G.; LOPES, FABIO J. da S.; MONTILLA ROSERO, ELENA; MOREIRA, GREGORI de A.; NAKAEMA, WALTER M.; NISPERUZA, DANIEL; ALEGRIA, DAIRO; MUNERA, MAURICIO; OTERO, LIDIA; PAPANDREA, SEBASTIAN; PALLOTA, JUAN V.; PAWELKO, EZEQUIEL; QUEL, EDUARDO J.; RISTORI, PABLO; RODRIGUES, PATRICIA F.; SALVADOR, JACOBO; SANCHEZ, MARIA F.; SILVA, ANTONIETA